ELT, or Extract, Load, Transform, is a common data processing workflow that involves extracting data from one or more sources, loading it into a target system or database, and then transforming it into a format that is suitable for analysis or other purposes. The goal of ELT is to make data available for analysis and decision-making as quickly and efficiently as possible, without the need for extensive data preparation or preprocessing.

One example of ELT in action is in the context of data warehousing. In this case, data from a variety of sources, such as transactional databases, flat files, or web applications, is extracted and loaded into a data warehouse. This data is then transformed into a format that is suitable for analysis, such as by applying transformations such as data cleaning, filtering, or aggregation. The transformed data can then be queried using SQL or other tools to generate reports, identify trends, or make data-driven decisions.

Another example of ELT is in the context of data lakes. In this case, data from a variety of sources is extracted and loaded into a central repository, such as an object store or file system. This data is then transformed into a format that is suitable for analysis, such as by applying transformations such as data cleaning, filtering, or aggregation. The transformed data can then be queried using SQL or other tools to generate reports, identify trends, or make data-driven decisions.

Overall, ELT is a powerful data processing workflow that enables organizations to quickly and efficiently make data available for analysis and decision-making. By extracting, loading, and transforming data from a variety of sources, ELT can help organizations unlock the value of their data and gain insights that would otherwise be difficult or impossible to obtain.